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Abstract

Introduction

Candidemia in critically ill patients is usually a severe and life-threatening condition
with a high crude mortality. Very few studies have focused on the impact of candidemia
on ICU patient outcome and attributable mortality still remains controversial. This
study was carried out to determine the attributable mortality of ICU-acquired candidemia
in critically ill patients using propensity score matching analysis.

Methods

A prospective observational study was conducted of all consecutive non-neutropenic
adult patients admitted for at least seven days to 36 ICUs in Spain, France, and Argentina
between April 2006 and June 2007. The probability of developing candidemia was estimated
using a multivariate logistic regression model. Each patient with ICU-acquired candidemia
was matched with two control patients with the nearest available Mahalanobis metric
matching within the calipers defined by the propensity score. Standardized differences
tests (SDT) for each variable before and after matching were calculated. Attributable
mortality was determined by a modified Poisson regression model adjusted by those
variables that still presented certain misalignments defined as a SDT > 10%.

Conclusions

ICU-acquired candidemia in critically ill patients is not associated with an increase
in either ICU or hospital mortality.

Introduction

Candida spp. are the fourth most common cause of bloodstream infection in hospitalized patients
[1-4] and a leading cause of invasive fungal infection in the ICU setting [1]. Candidemia in critically ill patients is usually a severe and life-threatening condition
with a high crude mortality ranging between 35% and 75% [5,6]. In contrast, candidemia-attributable mortality is unclear, with studies showing
high rates of 14.5% to 49% and others failing to demonstrate any significant increase
in mortality [7].

Infection caused by Candida spp. develops in patients with multiple risk factors and severe underlying diseases,
so that it is difficult to distinguish mortality attributed to candidemia from mortality
related to the underlying illness. Thus, there is an interest in conducting case-control
or matched cohort studies in which attributable mortality is obtained after matching
and adjusting for confounding variables. Despite these techniques it seems almost
impossible to match all factors (observable and unobservable) than can potentially
have an influence upon mortality. This obstacle was overcome by Rosenbaum and Rubin
[8] in 1983 who suggested matching 'cases' and 'controls' solely on their 'propensity
score' - the estimated probability of being a 'case' given observable characteristics.
In a scenario of ICU-acquired candidemia, each patient with candidemia is matched
to a patient without candidemia who is most similar in terms of being a candidemic
patient, where this probability is calculated on the basis of individual characteristics.
Once the two groups are formed, the average effect is estimated for each outcome by
simply computing the difference in means between the two groups. In recent years,
the use of propensity score analysis in observational studies has increased considerably
[9].

This study was designed to determine the attributable mortality of ICU-acquired candidemia
in non-neutropenic critically ill patients using propensity score matching analysis.

Materials and methods

Design and study population

This was a prospective, cohort, observational and multicenter study to assess candidemia-attributable
mortality in non-neutropenic adult ICU patients. Patients over the age of 18 years
who were admitted for at least seven days to 36 medical-surgical ICUs of 32 tertiary
care hospitals in Spain, three in Argentina, and one in France between April 2006
and June 2007 were eligible. Exclusion criteria were age under 18 years, neutropenia
defined as a total leukocyte count ≤500/mm3 for more than three weeks, life expectancy of less than a week, pregnant women and
nursing mothers, fungal infections other than those caused by Candida spp., patients who had Candida spp. isolation or were treated with antifungal drugs during the first seven days of
ICU admission, and refusal to give informed consent. The study protocol was approved
by the Ethics Committee of the participating centers and informed consent was obtained
from patients or patient's representatives to participate in the study and publication
of results.

Data collection

For all patients during their ICU stay, screening for Candida colonization was performed once a week using routine samples from a digestive focus
(feces and gastric or pharyngeal aspirates), urine, skin, respiratory samples and
peripheral blood. Other samples from vascular catheters, wound or drainage exudates,
or other infected foci could be obtained at the discretion of the attending physician.
Results were considered positive in the presence of Candida growth in the culture medium. Candidemia was defined as at least one blood culture
positive for Candida spp. The onset of candidemia was defined as the day when the first positive blood
culture was obtained. The different Candida isolates were identified at the species level. All catheter tips removed were cultured
in blood agar and Sabouraud Dextrose agar (Difco Laboratories, Detroit, Mich.) by
the Maki roll plate technique [10]. Catheter-related candidemia required the isolation of the same Candida species from blood and catheter segment when the semiquantitative catheter tip culture
yielded more than 15 colony-forming units (CFU) [11]. Candida peritonitis is defined as the isolation of Candida spp. in a peritoneal sample obtained by laparotomy or percutaneous puncture in patients
with associated clinical findings. Time to antifungal therapy was defined as the time
interval between when the first positive Candida blood culture was obtained and the time when antifungal therapy was initiated. We
measured time to antifungal initiation in 24-hour increments and categorized these
times as day 0 (0 to 24 hours), day one (24 to 48 hours), day three (48-72 hours)
and so on. Due to the observational aspect of this study, the choice of antifungal
agent was left to the discretion of the treating physicians. The use of fluconazole
was assumed to be inappropriate if it was prescribed for fungal bloodstream infections
caused by C. Krusei or C. glabrata (if it was resistant or no susceptibility testing was done). Patients were followed
until discharge from the ICU, discharge from the hospital, or death. The following
variables were recorded: age, sex, date of ICU admission, dates of ICU and hospital
discharge, reason for ICU admission, underlying diseases, concomitant infections,
previous treatment with antibiotics or corticosteroids, and risk factors. According
to diagnoses at the time of ICU admission, patients were classified as surgical, trauma,
or medical. Type of surgery (abdominal, non abdominal, elective, urgent) and number
of major procedures performed before and during ICU stay were recorded. Medical patients
undergoing major surgery during ICU stay were considered surgical patients. Underlying
diseases included insulin-dependent diabetes mellitus, neurological conditions, chronic
obstructive pulmonary disease (COPD), chronic liver disease, chronic renal failure
and severe heart failure. Risk factors included the following: treatment with corticosteroids
with a daily dose equivalent to 20 mg prednisone for at least two weeks, use of broad
spectrum antibiotics or antimicrobial drugs within ten days prior to the study, use
of mechanical ventilation and urinary catheter in place on the day of enrollment.
Central venous catheters, arterial catheters, total parenteral nutrition, enteral
nutrition and renal replacement therapy were also recorded.

The Acute Physiology and Chronic Health Evaluation (APACHE II) score [12] and the Sequential Organ Failure Assessment (SOFA) score [13] were recorded on ICU admission, once a week thereafter, and at the time of starting
antifungal treatment. Sepsis, severe sepsis, and septic shock were defined according
to international sepsis definitions [14].

Statistical analysis

Categorical variables are expressed as frequencies and percentages, and continuous
variables as mean and standard deviation (SD) when data followed a normal distribution,
or as median and interquartile (IQR) (25th to 75th percentile) range when distribution
departed from normality. Categorical variables were compared with the chi-square (χ2) test or the Fisher's exact probability test, the means by the Student's t test and the medians by the Kruskal-Wallis test. Risk factors for death among candidemic
ICU patients were determined using the Cox proportional-hazard regression model. The
probability of developing candidemia was estimated using a multivariate logistic regression
model that incorporated demographic data, length of ICU stay, severity indexes, comorbidities,
and risk factors [15]. Variables with statistical significance in univariate analysis (P < 0.2) and variables of clinical relevance were included in the model. We judged that
estimation of missing data was not required since in 91.6% of the patients (1,014)
all data were complete (three patients with candidemia had missing data). The discriminatory
power of the model was evaluated by the Hosmer-Lemeshow goodness-of-fit test and calculating
the area under the receiver operating characteristics (ROC) curve (AUC). The model
was considered to have good discriminatory power when AUC was greater than or equal
to 0.80. Each patient with ICU-acquired candidemia was matched to the two control
patients with the nearest available Mahalanobis metric matching within calipers determined
by the propensity score. The caliper was defined as one quarter of the standard deviation
of the logit of the propensity score [16]. To determine the effectiveness of propensity score matching in controlling for differences
between patients with and without candidemia, the standardized differences tests (SDT)
were calculated for each variable before and after matching. The McNemar's test was
used to compare crude mortality in the matched samples. Candidemia-attributable mortality
was determined by a modified Poisson regression model with a robust error variance
adjusted by those variables that still presented certain misalignment defined as a
SDT > 10% [17-19]. Statistical analyses were performed with SPSS 15.0 (SPSS Inc., Chicago, Illinois)
and SAS 9.1 software (SAS Institute, Inc, Cary, NC).

Results

A total of 1,107 patients fulfilled the inclusion criteria and were included in the
study. Thirty-eight patients (3.4%) developed candidemia, with an incidence rate of
34.3 episodes of candidemia per 1,000 ICU patients and 1.48 episodes per 1,000 days
of ICU stay (95% CI 1.05 to 2.03 episodes per 1,000 patient-days). Baseline characteristics
of the study population according to the presence or absence of candidemia are shown
in Table 1.

Table 1. Characteristics of the study patients according to infection status.

The median duration from ICU admission to the onset of candidemia was 14 days (IQR
8.75 to 19.25 days). Abdominal surgery was the diagnosis on ICU admission in 31 (81.6%)
patients. Infections were caused by C. albicans in 22 episodes, C. parapsilosis in nine, C. tropicalis in three, C. glabrata in two, C. krusei in one and Candida ssp. in one. The distribution of variables including severity indexes, ICU length of
stay and mortality was similar in patients with candidemia caused by C. albicans and non-albicans Candida spp. As for the source of candidemia, infection of unknown origin was reported as the
most frequent (n = 29, 76.3%) followed catheter-related infections (n = 8, 21.1%). One patient had candidemia and peritonitis concomitantly (2.6%). Multifocal
colonization was a clinically relevant condition which was present in up to 91.4%
of all patients before the development of candidemia. C. parapsilosis was the most frequently isolated species among the non-albicans spp in bloodstream
infections and had a lower crude mortality (33%). Seven patients were not given antifungals
and all of them died. Thirty-one patients were given antifungal treatment and in all
cases the selection of the antifungal drug was appropriate according to the Candida spp. isolated. The following antifungal agents were used either as the sole agent throughout
the course of treatment, or in a sequential pattern resulting in the use of multiple
agents for a single episode (that is, de-escalation): fluconazole was used most often
(n = 21, 67.7%), followed by amphotericin B-based (BL/CL) preparations (n = 7, 22.6%), caspofungin (n = 6, 19.4%) and voriconazole (n = 4, 12.9%). The median time for initiation of antifungal therapy was one day (IQR
0 to 4 days). In two patients with candidemia (5.7%), antifungal treatment was administered
preemptively on the same day as material for blood culture was collected (day 0).
The rest of the patients with candidemia were treated with targeted antifungal treatment
after notification of a positive Candida blood culture result. Nearly one third of the episodes (n = 12, 31.6%) were treated between 24 and 48 hours (day one) after blood cultures were
obtained (in some cases before final Candida species identification).

The crude ICU mortality rate was 52.6% (20/38) in patients with candidemia and 20.6%
(220/1,069) in those without candidemia (P < 0.001). The crude hospital death rate was 55.3% (21/38) and 29.6% (314/1,069) among
patients with and without candidemia, respectively (P = 0.01).

The logistic regression model used in propensity score analysis showed a high discriminatory
power with an AUC value of 0.902. The standardized differences in the demographic
and clinical variables of interest nearly disappeared when matched patients were analyzed
(Table 2). In the matched study sample, the crude ICU mortality was 51.4% in the group with
candidemia and 37.1% in the group without candidemia (P = 0.222). The crude hospital mortality rate was 54.3% among candidemic patients and
50% among non-candidemic patients (P = 0.680). After controlling residual confusion using a Poisson regression model, the
risk of ICU candidemia-attributable mortality was relative risk (RR) 1.298 (95% CI
0.88 to 1.98) and the risk of hospital candidemia-attributable mortality was RR 1.096
(95% CI 0.68 to 1.69).

Table 2. Propensity score-matched patients with and without candidemia.

Discussion

General findings obtained in this study, including an incidence rate of 34.3 episodes
of candidemia per 1,000 ICU patients and a crude mortality rate of 52.6% are consistent
with previously published data [5,20]. Attributable ICU mortality to Candida-related bloodstream infection varies largely and has been estimated to be non-significantly
different from that of patients without candidemia or to be higher by 5% to 40% [7,21,22]. For this reason, analysis in matched cohort samples is recommended, although only
seven studies assessing attributable mortality of candidemia have been previously
published [23]. Important limitations are related to methodological heterogeneity, including study
design (usually retrospective), study population (patients hospitalized in ICUs and
hospital wards and those undergoing transplantation), source of data (hospital or
microbiological databases) and matching criteria mainly based on underlying disease
and comorbidity, rather than on all possible factors that may influence mortality.
Moreover, severity scores and length of ICU stay were not considered.

In the present prospective and multicenter study, candidemia was not associated with
an increase in either ICU or hospital mortality. This finding may be explained by
the use of propensity matching score analysis to control for all potential confounding
variables related to the development of Candida spp. bloodstream infection. Matching was highly effective and both candidemic and
non-candidemic groups had similar characteristics, differing only in the development
of bloodstream infection.

These favorable results could be attributed to earlier treatment of bloodstream infection
and better monitoring (weekly surveillance sampling). An early start of appropriate
antifungal therapy (within the critical time-frame of the first 24 to 48 hours) is
crucial for the reduction of bloodstream infection-related mortality [24,25]. This observation confirms the importance of an empirical and preemptive strategy.
Another important factor is the rapid reporting of the microbiological results (positive
bloodstream infection and species identification) in order to initiate targeted antifungal
treatment or to modify previous empirical antifungal agents. In our study, the median
time for initiation of antifungal therapy was one day (IQR 0 to 4 days). In 36.9%
of patients it was initiated within 48 hours of obtaining the first positive blood
culture (5.2% of them as preemptive treatment). Another important problem preventing
the earlier recognition and treatment of candidemia is the lack of specific clinical
findings. Different prediction rules based on a variety of risk factors, including
Candida species colonization are recommended to identify patients at high risk for fungal
bloodstream infections [26,27]. High density colonization can be used to identify patients who may benefit from
preemptive antifungal treatment in the appropriate clinical setting [28]. In our study multifocal colonization was a clinically relevant condition which was
present in up to 92.1% of all patients before the development of candidemia and there
was a high similarity between causative and colonized strains; this could guide appropriate
antifungal treatment. Additionally, an appropriate antifungal agent was administered
in all treated patients and this may also contribute to increased survival. Moreover,
earlier replacement of central venous catheters, common practice in ICU's, could also
reduce mortality. On the other hand, antifungal therapy was initiated in 180 non-candidemic
high-risk patients (n = 180/1,069, 16.8%) as an empiric or preemptive strategy. This antifungal strategy
has not been evaluated in previous articles estimating candidemia-attributable mortality.
As this can be a major confounding factor, empirical or preemptive treatment was introduced
in the logistic regression model. There was no difference in this variable in the
propensity score-matched patients as we list in Table 2. It has been shown that ICU patients with candidemia have a substantially higher
severity of illness on the day of diagnosis compared with the ICU admission day [29]. This is also an important disadvantage for matching because severity scores are
determined on ICU admission but not at the time of developing candidemia. However,
after matching by the propensity score, APACHE II and SOFA severity scores recorded
weekly did not vary significantly between cases and controls.

The crude ICU mortality in patients with candidemia in our study is 52.6% with a median
of five days from the diagnosis until death. Due to the high mortality and poor prognosis
of patients with Candida spp. bloodstream infection, there is a strong need to identify predictors of death.
Factors associated with fatal outcome, such as age, malnutrition, delay in removal
of central venous catheter, candidemia caused by non-albicans Candida spp. and delay in starting antifungal therapy have been extensively recognized [25,30-32]. However, in our study only APACHEII score at the time of diagnosis of candidemia
was a predictor of mortality.

The lack of differences in mortality rates between the groups of C. albicans and non-albicans Candida spp. may be explained by different reasons: i) Adequacy of antifungal therapy with
all candidemias caused by Candida spp. with a possible decreased sensitivity or resistance to azoles being treated with
candins or different formulations of amphotericin B; ii) a relatively high percentage
of cases caused by C. parapsilosis (23.7%), associated with lower mortality [1,33,34]; and iii) a systematic removal policy for central venous catheters.

Two frequently cited studies [25,29] have shown an association between mortality and delay in start antifungal treatment.
It should be noted that in both studies, the majority of episodes of candidemia occurred
in patients admitted to hospital wards, with an in-hospital mortality rate of about
30%. In our study, all patients had ICU-acquired candidemia, with a higher crude mortality.
It may be possible that antifungal treatment is started earlier in critically ill
patients admitted to the ICU than in less severely ill patients in the hospital wards
where strict protocols of microbiological surveillance are not established.

Despite the fact that more than 1,000 patients were recruited, the small number of
candidemias related to the incidence of invasive candidiasis in the critically ill
patient is a limitation of the study and might have compromised our power to detect
differences in mortality between cases and controls. Additionally, the sensitivity
of blood cultures for candidiasis is low (approximately 60%). This could impact the
actual incidence of candidemia and also the overall mortality in the ICU. Therefore,
there is a possibility that patients with candidemia that may have had identical matching
risk factors but negative blood culture were considered matching controls instead
of cases.

Conclusions

In summary, ICU-acquired candidemia in critically ill patients was not associated
with an increase of either ICU or hospital mortality. APACHE II at the time of diagnosis
of candidemia was the only predictor of death in patients with candidemia.

Key messages

• Candidemia was not associated with an increase in either ICU or hospital mortality.

• The use of propensity score matching analysis to control for all potential confounding
variables allowed the assessment of candidemia-attributable mortality in critically
ill patients.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

FGdM was involved in data collection, contributed to writing of the manuscript and
performed the statistical analysis. CL contributed to the conception and design of
the study and revision of the manuscript. SRS contributed to the study design and
revision of the manuscript. PS was involved in data management and data analysis.
All authors read and approved the final version of the manuscript.

Acknowledgements

The authors thanks Marta Pulido, MD, for editing the manuscript and editorial assistance
and Ignacio Ferreira-González, MD, for their methodological support. Supported by
a grant from Fundación de Medicina Intensiva Valme. Sevilla, Spain.

Members of the CAVA I Study Group (participating centers in parenthesis):